Enhancing Optical Character Recognition (OCR) Accuracy in Healthcare Prescription Processing using Artificial Neural Networks

The healthcare industry continues to depend extensively on paper-based prescriptions, frequently transmitted through fax systems, to ensure compliance with stringent security and regulatory standards. This reliance introduces significant challenges to manual processing, which is often labor-intensive, error-prone, and inefficient, thereby increasing the risk of medication errors and compromising patient safety. Optical Character Recognition (OCR) technology offers a promising solution by automating the conversion of handwritten and printed prescription images into machine-readable text, thereby enhancing the accuracy and efficiency of healthcare workflows. This paper presents a detailed review of OCR methodologies, focusing on feature extraction, classification, and segmentation techniques that are critical for achieving high recognition accuracy in complex handwritten texts, such as medical prescriptions. By leveraging advanced pattern recognition approaches, including neural networks and hybrid models, OCR systems can significantly reduce manual effort and errors in prescription processing. This study emphasizes the importance of selecting relevant feature extraction methods and robust classification algorithms to handle the variability and noise inherent in re-faxed prescriptions, thereby improving the reliability of automated prescription recognition systems. This study aims to guide and update researchers and practitioners in developing efficient OCR solutions tailored to the healthcare domain, ultimately contributing to safer and more efficient medication administration processes.